Precise intelligent recognition method and application of coal-power-chemical industry sites characteristics in Yellow River Basin

DONG Jihong; WANG Libing; FENG Xiaotong; WANG Lei; LIU Feng; ZHAI Wen; THOMAS Kienberger

doi:10.13225/j.cnki.jccs.2023.1212

Abstract：The Yellow River Basin is an energy basin that has the dual responsibility of ecological environment gov-ernance and economic and social development.The precise and intelligent recognition of the categories,numbers and characteristics of coal-related industrial sites is a key basic issue for energy resources-low carbon development-ecological protection in the basin.This study integrated the multi-source data and deep learning algorithms to precisely analyze the characteristics of coal-based sites in 13 large-scale coal-fired power bases in the Yellow River Basin from the basin-base-site scale,obtained the high-precision and high-quality background information of coal-power bases,and proposed a new method of real-time real-scene intelligent recognition of spatial characteristics of coal-related industries.In this study,①Multi-source data such as Google image,GF-6 image,Sentinel-2 image,etc.were collected as coal-based site samples from 13 large-scale coal-fired power bases to build four datasets of coal mine sites(open-pit),coal mine sites(under-ground),coal-power sites,and coal chemical sites,covering 21 categories of samples.According to each type of sample,6×10 samples were set for each hexagonal cell,totaling 1260 site samples.The confidence interval of the optimal sample number-highest recognition efficiency-optimal recognition model was 80%-86%.②A coal-based site classification quantitative model(CSCQM)and a coal-based site range characteristic model(CSRCM)were established.The average accuracy of the models was 0.837.The background information of coal-related industrial sites in the Yellow River Basin were clarified,and a high-precision site intelligent recognition method based on Google image base map overlaying site intelligent recognition model calculation results was proposed.③The precise background data of the Shendong coal-power industrial agglomeration area in the basin were analyzed.Analyzed by remote sensing based ecological index(RSEI),the surface ecological quality of the 2 km core area of coal-based sites was significantly affected by coal mine and coal-power industries,while the 5 km buffer zone was not significantly affected,and the 8 km control zone was basically not affected by coal mine and coal power industries.Thus,the low-carbon pathways such as dynamic remediation and key management by region and stage were proposed.④The precise background data of the Ningdong coal-power-chemical industrial agglomeration area in the basin were analyzed.In 2022,the area of coal mine sites covered an area of 17.81 km2,accounting for 34.1%of the total area,the area of coal chemical sites covered an area of 22.3 km2,accounting for 42.6%of the total area,and the area of coal-power sites covered an area of 12.2 km2,accounting for 23.3%of the total area.The area ratio was coal chemical sites>coal mine sites>coal-power sites.Then,using the PSR(Pressure-State-Response)model,the comprehensive score of risk management was obtained as 53.93 points,which was 27.2%higher than that in 2003.A zoning management mode of ecological maintenance zone,production monitoring and early warning zone,dam-age repair and reconstruction zone,and other regulation zone were implemented.The study provided some technical meth-ods and practical support for the potential pollution control,site management and regional ecological restoration of coal-related industrial sites.